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Center for Global Research Data

Harnessing genetic interactions to advance precision cancer medicine.

Lead Investigator: Joo Sang Lee, Samsung Medical Center, South Korea
Title of Proposal Research: Harnessing genetic interactions to advance precision cancer medicine.
Vivli Data Request: 5096
Funding Source: Employment Contracts – The proposed research is funded by the cross-institutional collaboration grant between Samsung Medical Center and Sungkyunkwan University.
Potential Conflicts of Interest: None.

Summary of the Proposed Research:

Current precision oncology approaches are mainly resorting to genome sequencing data, aiming to identify actionable mutations. Here we introduce a complementary approach by incorporating the transcriptomics data to advance precision cancer medicine. We analyze large-scale cancer genomics/transcriptomics data collected in the public domain (n>15,000) and use them to guide the treatments for individual patients based on their genomics/transcriptomic profiles.

Statistical Analysis Plan

We will first analyze large-scale public transcriptomics and genomics from cancer patient data to infer genetic interactions of the therapy targets used in TEMPUS cohort. We are focusing the samples in the public domain where we have transcriptomics, genomics, and clinical information available (including the Cancer Genome Atlas (TCGA), International Cancer Genomics Consortium (ICGC)). We will then use the genetic interactions partners of the drug targets in the TEMPUS cohort to make predictions which patient would respond to the therapy using pre-treatment genomics and transcriptomics data of the TEMPUS cohort. We will check whether our prediction score is significantly higher in responders (using Wilcoxon test), and perform receiver operating characteristics (ROC)/precision-recall analysis to evaluate the predictive accuracy of our approach. For TEMPUS cohort, we need genomics, transcriptomics, clinical information including the therapy response. We will try our best to avoid imputation for the missing values in case we have sufficient number of samples even after we remove the samples of missing values; in the case we do not have enough samples for statistical analysis, we will perform the imputation.

Requested Studies:

Integrated genomic profiling expands clinical options for cancer patients
Sponsor: Tempus Labs, Inc.
Study ID: T19.01

Public Disclosure:

Synthetic lethality-mediated precision oncology via the tumor transcriptome. Lee, Joo Sang et al.
Cell, Volume 184, Issue 9, 2487 – 2502.e13. DOI: